{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:31:07Z","timestamp":1772253067968,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2018,3,9]],"date-time":"2018-03-09T00:00:00Z","timestamp":1520553600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Developing efficient Artificial Intelligence (AI)-enabled systems to substitute the human role in non-destructive testing is an emerging topic of considerable interest. In this study, we propose a novel hammering response analysis system using online machine learning, which aims at achieving near-human performance in assessment of concrete structures. Current computerized hammer sounding systems commonly employ lab-scale data to validate the models. In practice, however, the response signal patterns can be far more complicated due to varying geometric shapes and materials of structures. To deal with a large variety of unseen data, we propose a sequential treatment for response characterization. More specifically, the proposed system can adaptively update itself to approach human performance in hammering sounding data interpretation. To this end, a two-stage framework has been introduced, including feature extraction and the model updating scheme. Various state-of-the-art online learning algorithms have been reviewed and evaluated for the task. To conduct experimental validation, we collected 10,940 response instances from multiple inspection sites; each sample was annotated by human experts with healthy\/defective condition labels. The results demonstrated that the proposed scheme achieved favorable assessment accuracy with high efficiency and low computation load.<\/jats:p>","DOI":"10.3390\/s18030833","type":"journal-article","created":{"date-parts":[[2018,3,9]],"date-time":"2018-03-09T12:17:41Z","timestamp":1520597861000},"page":"833","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Computerized Hammer Sounding Interpretation for Concrete Assessment with Online Machine Learning"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6680-1201","authenticated-orcid":false,"given":"Jiaxing","family":"Ye","sequence":"first","affiliation":[{"name":"National Metrology Institute of Japan (NMIJ), The National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 2, Tsukuba 305-8568, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takumi","family":"Kobayashi","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Center (AIRC), The National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8561, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masaya","family":"Iwata","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Center (AIRC), The National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8561, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8766-7611","authenticated-orcid":false,"given":"Hiroshi","family":"Tsuda","sequence":"additional","affiliation":[{"name":"National Metrology Institute of Japan (NMIJ), The National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba Central 2, Tsukuba 305-8568, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahiro","family":"Murakawa","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Center (AIRC), The National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8561, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,3,9]]},"reference":[{"key":"ref_1","unstructured":"Sansalone, M.J., and William, B.S. 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